R.Version()$platform
[1] "aarch64-apple-darwin20"
$arch
[1] "aarch64"
$os
[1] "darwin20"
$system
[1] "aarch64, darwin20"
$status
[1] ""
$major
[1] "4"
$minor
[1] "2.2"
$year
[1] "2022"
$month
[1] "10"
$day
[1] "31"
$`svn rev`
[1] "83211"
$language
[1] "R"
$version.string
[1] "R version 4.2.2 (2022-10-31)"
$nickname
[1] "Innocent and Trusting"The data was assessed with the formr survey framework (Arslan, Walther, and Tata 2020). The raw data was imported via the following code.
library(tidyverse)
library(ggforce)
library(lme4)
library(BFpack)
library(hrbrthemes)
library(patchwork)
library(brms)
library(viridis)
library(ggdist)
library(tidybayes)
library(here)
library(skimr)
load(here("data/teachers_study1_N40.RData"))
skim(study1)
set.seed(25051982)# wrangle information on the plot type, ES, ...
plot_info <- study1 %>%
pivot_longer(2:195, names_to = "variables", values_to = "values",
values_transform = as.character) %>%
dplyr::filter(str_detect(variables, "plot")) %>%
# we only need the rows with info on plots
tidyr::separate(col = values, into = c("type", "axis", "effsize"),
# separate the info into three columns
sep = "_", remove = F) %>%
dplyr::mutate(plot = variables, # rename variables for later join
type = paste(type, axis, sep = "_")) %>%
dplyr::select(-variables, -axis)
# wrangle answers to items on each page
item_values <- study1 %>%
dplyr::select(-c(topic:itemo)) %>%
pivot_longer(2:169, names_to = "variables", values_to = "values",
values_transform = as.character) %>%
dplyr::mutate(variables = case_when( # recode variable names that have
variables == "sensi_6" ~ "sensi_06", # accidentally been labeled
variables == "acccl_6" ~ "acccl_06", # without zero
variables == "accu3_6" ~ "accu3_06",
variables == "accov_6" ~ "accov_06",
variables == "diffi_6" ~ "diffi_06",
variables == "infor_6" ~ "infor_06",
variables == "value_6" ~ "value_06",
TRUE ~ variables
)) %>%
dplyr::mutate(plot = paste0("plotx_", str_sub(variables, -2, -1)),
# create variable for later join
variables = str_sub(variables, 1, -4)) %>%
# rename variable names to get a data set
# with one line per participant per page
pivot_wider(id_cols = c(session, plot), names_from = "variables",
values_from = "values")
skim(plot_info)| Name | plot_info |
| Number of rows | 960 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| character | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
| values | 0 | 1 | 17 | 24 | 0 | 24 | 0 |
| type | 0 | 1 | 13 | 19 | 0 | 4 | 0 |
| effsize | 0 | 1 | 3 | 4 | 0 | 6 | 0 |
| plot | 0 | 1 | 8 | 8 | 0 | 24 | 0 |
skim(item_values)| Name | item_values |
| Number of rows | 960 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1.0 | 64 | 64 | 0 | 40 | 0 |
| plot | 0 | 1.0 | 8 | 8 | 0 | 24 | 0 |
| sensi | 480 | 0.5 | 59 | 62 | 0 | 3 | 0 |
| acccl | 480 | 0.5 | 1 | 4 | 0 | 11 | 0 |
| accu3 | 480 | 0.5 | 1 | 3 | 0 | 48 | 0 |
| accov | 480 | 0.5 | 1 | 3 | 0 | 52 | 0 |
| diffi | 0 | 1.0 | 1 | 1 | 0 | 7 | 0 |
| infor | 0 | 1.0 | 1 | 1 | 0 | 7 | 0 |
| value | 0 | 1.0 | 1 | 1 | 0 | 7 | 0 |
# join the two data sets
study1_w <- full_join(plot_info, item_values,
by = c("session", "plot")) %>%
# by participant and page (plot)
dplyr::select(-values) %>%
dplyr::mutate(rating_cl = as.numeric(acccl), # some var need to be defined as
rating_u3 = as.numeric(accu3), # numeric again
rating_ov = as.numeric(accov),
diffi = as.numeric(diffi),
infor = as.numeric(infor),
value = as.numeric(value),
effsize = as.numeric(effsize),
effsize_cl = case_when(
# there is no negative Cliff's Delta, so we have to compute
# two transformations
effsize > 0 ~ (((2*pnorm(effsize/2))-1)/pnorm(effsize/2)),
# transform the actual effect size Cohen's d to Cliff's Delta
effsize < 0 ~ (- (((2*pnorm(abs(effsize)/2))-1)/pnorm(abs(effsize)/2)))
# transform the actual effect size Cohen's d to Cliff's Delta
# and make it negative as in the item
),
effsize_u3 = 1-pnorm(effsize), # reverse so that it fits the direction of the U3 item
# transform the actual effect size Cohen's d to Cohen's U3
effsize_ov = 2 * pnorm(-abs(effsize) / 2),
# transform the actual effect size Cohen's d to overlap
# actual difference of rating relative to depicted effectsize
diff_cl = (rating_cl - effsize_cl)/2,
# actual difference of rating relative to depicted effectsize
diff_u3 = (rating_u3/100) - effsize_u3,
# actual difference of rating relative to depicted effectsize
diff_ov = (rating_ov/100) - effsize_ov,
diffi_normed = ((diffi - 1) / 3) - 1, # transform item to -1 to 1
infor_normed = ((infor - 1) / 3) - 1, # transform item to -1 to 1
value_normed = ((value - 1) / 3) - 1) %>% # transform item to -1 to 1
group_by(session) %>%
mutate(rating_ov_missconcept = median(rating_ov, na.rm = T) < 68.9,
rating_u3_missconcept = median(rating_u3, na.rm = T) < 21.2) %>%
ungroup() %>%
mutate(rating_u3_filtered = ifelse(rating_u3_missconcept == T, NA, rating_u3),
rating_ov_filtered = ifelse(rating_ov_missconcept == T, NA, rating_ov),
diff_u3_filtered = (rating_u3_filtered/100) - effsize_u3,
diff_ov_filtered = (rating_ov_filtered/100) - effsize_ov,
sensi_binary = ifelse(is.na(sensi), # 1 if NOT "equal"
NA,
as.numeric(!grepl("equal", sensi))),
sensi_ordinal = ordered(factor(substr(sensi, 55, 100)),
levels = c("inferior",
"equal",
"superior")),
sensi_binary_filtered = case_when(sensi_ordinal == "equal" ~ 0,
(sensi_ordinal == "inferior" &
effsize < 0) |
(sensi_ordinal == "superior" &
effsize > 0) ~ as.numeric(NA),
(sensi_ordinal == "inferior" & # was not there
effsize > 0) |
(sensi_ordinal == "superior" &
effsize < 0) ~ 1,
TRUE ~ as.numeric(NA)), # was 1
sensi_correct = case_when(sensi_ordinal == "equal" ~ "judged equal",
(sensi_ordinal == "inferior" &
effsize < 0) |
(sensi_ordinal == "superior" &
effsize > 0) ~ "wrong direction",
(sensi_ordinal == "inferior" & # was not there
effsize > 0) |
(sensi_ordinal == "superior" &
effsize < 0) ~ "right direction",
TRUE ~ NA_character_),
effsize_abs = abs(effsize))
skim(study1_w)| Name | study1_w |
| Number of rows | 960 |
| Number of columns | 34 |
| _______________________ | |
| Column type frequency: | |
| character | 8 |
| factor | 1 |
| logical | 2 |
| numeric | 23 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1.0 | 64 | 64 | 0 | 40 | 0 |
| type | 0 | 1.0 | 13 | 19 | 0 | 4 | 0 |
| plot | 0 | 1.0 | 8 | 8 | 0 | 24 | 0 |
| sensi | 480 | 0.5 | 59 | 62 | 0 | 3 | 0 |
| acccl | 480 | 0.5 | 1 | 4 | 0 | 11 | 0 |
| accu3 | 480 | 0.5 | 1 | 3 | 0 | 48 | 0 |
| accov | 480 | 0.5 | 1 | 3 | 0 | 52 | 0 |
| sensi_correct | 480 | 0.5 | 12 | 15 | 0 | 3 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| sensi_ordinal | 480 | 0.5 | TRUE | 3 | sup: 177, inf: 159, equ: 144 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| rating_ov_missconcept | 0 | 1 | 0.22 | FAL: 744, TRU: 216 |
| rating_u3_missconcept | 0 | 1 | 0.55 | TRU: 528, FAL: 432 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| effsize | 0 | 1.00 | 0.00 | 0.56 | -0.80 | -0.50 | 0.00 | 0.50 | 0.80 | ▇▃▁▃▇ |
| diffi | 0 | 1.00 | 4.05 | 1.71 | 1.00 | 3.00 | 4.00 | 5.00 | 7.00 | ▇▆▆▆▇ |
| infor | 0 | 1.00 | 4.11 | 1.45 | 1.00 | 3.00 | 4.00 | 5.00 | 7.00 | ▅▅▇▇▅ |
| value | 0 | 1.00 | 4.13 | 1.49 | 1.00 | 3.00 | 4.00 | 5.00 | 7.00 | ▅▃▆▇▅ |
| rating_cl | 480 | 0.50 | 0.01 | 0.38 | -1.00 | -0.20 | 0.00 | 0.20 | 1.00 | ▂▆▇▃▁ |
| rating_u3 | 480 | 0.50 | 26.17 | 22.60 | 0.00 | 6.00 | 18.00 | 45.00 | 100.00 | ▇▃▃▁▁ |
| rating_ov | 480 | 0.50 | 71.63 | 28.63 | 0.00 | 65.00 | 80.00 | 90.00 | 100.00 | ▂▁▁▅▇ |
| effsize_cl | 0 | 1.00 | 0.00 | 0.34 | -0.47 | -0.33 | 0.00 | 0.33 | 0.47 | ▇▃▁▃▇ |
| effsize_u3 | 0 | 1.00 | 0.50 | 0.21 | 0.21 | 0.31 | 0.50 | 0.69 | 0.79 | ▇▃▁▃▇ |
| effsize_ov | 0 | 1.00 | 0.80 | 0.09 | 0.69 | 0.69 | 0.80 | 0.92 | 0.92 | ▇▁▇▁▇ |
| diff_cl | 480 | 0.50 | 0.01 | 0.18 | -0.54 | -0.06 | 0.00 | 0.07 | 0.64 | ▁▂▇▂▁ |
| diff_u3 | 480 | 0.50 | -0.24 | 0.29 | -0.79 | -0.49 | -0.21 | -0.03 | 0.69 | ▅▆▇▂▁ |
| diff_ov | 480 | 0.50 | -0.09 | 0.29 | -0.92 | -0.12 | 0.01 | 0.10 | 0.31 | ▂▁▁▇▅ |
| diffi_normed | 0 | 1.00 | 0.02 | 0.57 | -1.00 | -0.33 | 0.00 | 0.33 | 1.00 | ▇▆▆▆▇ |
| infor_normed | 0 | 1.00 | 0.04 | 0.48 | -1.00 | -0.33 | 0.00 | 0.33 | 1.00 | ▅▅▇▇▅ |
| value_normed | 0 | 1.00 | 0.04 | 0.50 | -1.00 | -0.33 | 0.00 | 0.33 | 1.00 | ▅▃▆▇▅ |
| rating_u3_filtered | 744 | 0.22 | 43.06 | 17.90 | 0.00 | 30.00 | 45.00 | 55.00 | 80.00 | ▂▃▇▇▂ |
| rating_ov_filtered | 588 | 0.39 | 82.60 | 16.81 | 0.00 | 80.00 | 85.00 | 90.00 | 100.00 | ▁▁▁▅▇ |
| diff_u3_filtered | 744 | 0.22 | -0.06 | 0.22 | -0.79 | -0.15 | -0.03 | 0.06 | 0.54 | ▁▂▇▅▁ |
| diff_ov_filtered | 588 | 0.39 | 0.02 | 0.17 | -0.92 | -0.02 | 0.05 | 0.10 | 0.31 | ▁▁▁▇▅ |
| sensi_binary | 480 | 0.50 | 0.70 | 0.46 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| sensi_binary_filtered | 507 | 0.47 | 0.68 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| effsize_abs | 0 | 1.00 | 0.50 | 0.25 | 0.20 | 0.20 | 0.50 | 0.80 | 0.80 | ▇▁▇▁▇ |
skim(study1_w %>%
select(diffi,infor,value,rating_u3, rating_ov, rating_cl))| Name | %>%(…) |
| Number of rows | 960 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| diffi | 0 | 1.0 | 4.05 | 1.71 | 1 | 3.0 | 4 | 5.0 | 7 | ▇▆▆▆▇ |
| infor | 0 | 1.0 | 4.11 | 1.45 | 1 | 3.0 | 4 | 5.0 | 7 | ▅▅▇▇▅ |
| value | 0 | 1.0 | 4.13 | 1.49 | 1 | 3.0 | 4 | 5.0 | 7 | ▅▃▆▇▅ |
| rating_u3 | 480 | 0.5 | 26.17 | 22.60 | 0 | 6.0 | 18 | 45.0 | 100 | ▇▃▃▁▁ |
| rating_ov | 480 | 0.5 | 71.63 | 28.63 | 0 | 65.0 | 80 | 90.0 | 100 | ▂▁▁▅▇ |
| rating_cl | 480 | 0.5 | 0.01 | 0.38 | -1 | -0.2 | 0 | 0.2 | 1 | ▂▆▇▃▁ |
sociodemographics <- read_delim("data/teachers_study1a.csv", delim = ";") %>%
select(session, mcstu, texpe, mcsub) %>%
# filter(!is.na(mcstu & texpe)) %>%
mutate(reply = session %in% c(study1_w$session)) %>%
filter(!reply == "FALSE") %>%
select(-reply) %>%
mutate(mcstu = as.factor(mcstu),
texpe = as.numeric(texpe),
mcsub = as.factor(mcsub),
subject_stem = grepl("1", mcsub),
subject_languages = grepl("2", mcsub),
subject_humanities_socialscience = grepl("3", mcsub),
subject_asthetic = grepl("4", mcsub))
skim(sociodemographics) # n = 15 participants checked "others" when asked which school type they teach at| Name | sociodemographics |
| Number of rows | 40 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| logical | 4 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| mcstu | 0 | 1 | FALSE | 6 | 6: 15, 1: 9, 2: 7, 3: 6 |
| mcsub | 0 | 1 | FALSE | 10 | 1: 8, 3: 8, 2: 7, 1, : 3 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| subject_stem | 0 | 1 | 0.48 | FAL: 21, TRU: 19 |
| subject_languages | 0 | 1 | 0.45 | FAL: 22, TRU: 18 |
| subject_humanities_socialscience | 0 | 1 | 0.50 | FAL: 20, TRU: 20 |
| subject_asthetic | 0 | 1 | 0.15 | FAL: 34, TRU: 6 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| texpe | 0 | 1 | 10.15 | 8.66 | 2 | 4 | 7 | 13.5 | 37.5 | ▇▃▂▁▁ |
study1_w_demo <- full_join(study1_w, sociodemographics, by = "session") %>%
mutate(schooltype_binary = ifelse(mcstu == 6, 1, 0)) # 6 = others
study1_w_type <- study1_w %>%
select(diffi, infor,value,rating_u3, rating_ov, rating_cl, session,
type, plot) %>%
gather(var, value, diffi, infor,value,rating_u3, rating_ov, rating_cl)%>%
mutate(variable = paste(var, type, sep = "_"))%>%
select(-type, -var)%>%
group_by(session, plot) %>%
spread(variable, value) %>%
ungroup()
skim(study1_w_type)| Name | study1_w_type |
| Number of rows | 960 |
| Number of columns | 26 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 24 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
| plot | 0 | 1 | 8 | 8 | 0 | 24 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| diffi_gardneraltman_xaxis | 720 | 0.25 | 3.59 | 1.87 | 1 | 2.00 | 3.0 | 5.00 | 7.0 | ▇▅▃▃▅ |
| diffi_halfeye_xaxis | 720 | 0.25 | 4.23 | 1.54 | 1 | 3.00 | 4.0 | 6.00 | 7.0 | ▅▇▆▆▇ |
| diffi_halfeye_yaxis | 720 | 0.25 | 4.55 | 1.58 | 1 | 3.00 | 5.0 | 6.00 | 7.0 | ▃▃▃▆▇ |
| diffi_raincloud_yaxis | 720 | 0.25 | 3.83 | 1.68 | 1 | 2.75 | 4.0 | 5.00 | 7.0 | ▇▆▆▅▆ |
| infor_gardneraltman_xaxis | 720 | 0.25 | 3.80 | 1.57 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▆▅▆▇▃ |
| infor_halfeye_xaxis | 720 | 0.25 | 4.19 | 1.34 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▃▆▇▇▅ |
| infor_halfeye_yaxis | 720 | 0.25 | 4.40 | 1.35 | 1 | 3.00 | 4.5 | 5.00 | 7.0 | ▂▅▇▇▆ |
| infor_raincloud_yaxis | 720 | 0.25 | 4.04 | 1.48 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▅▃▇▆▅ |
| rating_cl_gardneraltman_xaxis | 843 | 0.12 | 0.03 | 0.40 | -1 | -0.20 | 0.0 | 0.20 | 1.0 | ▂▅▇▃▁ |
| rating_cl_halfeye_xaxis | 834 | 0.13 | 0.00 | 0.36 | -1 | -0.20 | 0.0 | 0.20 | 1.0 | ▁▇▇▂▁ |
| rating_cl_halfeye_yaxis | 842 | 0.12 | 0.01 | 0.40 | -1 | -0.20 | 0.0 | 0.20 | 1.0 | ▂▇▇▃▁ |
| rating_cl_raincloud_yaxis | 841 | 0.12 | 0.03 | 0.38 | -1 | -0.20 | 0.0 | 0.20 | 0.8 | ▁▃▇▆▂ |
| rating_ov_gardneraltman_xaxis | 843 | 0.12 | 68.63 | 30.39 | 0 | 60.00 | 80.0 | 90.00 | 100.0 | ▂▁▂▅▇ |
| rating_ov_halfeye_xaxis | 834 | 0.13 | 74.73 | 26.46 | 0 | 70.00 | 85.0 | 90.00 | 100.0 | ▂▁▁▃▇ |
| rating_ov_halfeye_yaxis | 842 | 0.12 | 70.08 | 30.92 | 0 | 66.25 | 80.0 | 90.00 | 100.0 | ▂▁▁▅▇ |
| rating_ov_raincloud_yaxis | 841 | 0.12 | 72.83 | 26.57 | 0 | 67.50 | 80.0 | 90.00 | 100.0 | ▂▁▁▅▇ |
| rating_u3_gardneraltman_xaxis | 843 | 0.12 | 26.56 | 21.61 | 0 | 7.00 | 22.0 | 45.00 | 100.0 | ▇▃▅▁▁ |
| rating_u3_halfeye_xaxis | 834 | 0.13 | 26.73 | 23.01 | 0 | 5.25 | 20.0 | 48.75 | 80.0 | ▇▂▂▃▁ |
| rating_u3_halfeye_yaxis | 842 | 0.12 | 27.08 | 23.33 | 0 | 7.00 | 15.0 | 50.00 | 75.0 | ▇▁▂▃▁ |
| rating_u3_raincloud_yaxis | 841 | 0.12 | 24.28 | 22.54 | 0 | 5.50 | 15.0 | 40.00 | 100.0 | ▇▃▂▁▁ |
| value_gardneraltman_xaxis | 720 | 0.25 | 3.80 | 1.51 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▆▅▇▇▃ |
| value_halfeye_xaxis | 720 | 0.25 | 4.31 | 1.40 | 1 | 3.75 | 5.0 | 5.00 | 7.0 | ▃▃▆▇▅ |
| value_halfeye_yaxis | 720 | 0.25 | 4.38 | 1.39 | 1 | 4.00 | 5.0 | 5.00 | 7.0 | ▂▂▅▇▃ |
| value_raincloud_yaxis | 720 | 0.25 | 4.03 | 1.58 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▆▃▆▇▅ |
# abstract metric
mean(study1_w_type$rating_cl_gardneraltman_xaxis, na.rm=TRUE)[1] 0.02905983sd(study1_w_type$rating_cl_gardneraltman_xaxis, na.rm=TRUE)[1] 0.3976352mean(study1_w_type$rating_cl_halfeye_xaxis, na.rm=TRUE)[1] -0.003174603sd(study1_w_type$rating_cl_halfeye_xaxis, na.rm=TRUE)[1] 0.3550632mean(study1_w_type$rating_cl_halfeye_yaxis, na.rm=TRUE)[1] 0.005084746sd(study1_w_type$rating_cl_halfeye_yaxis, na.rm=TRUE)[1] 0.402948mean(study1_w_type$rating_cl_raincloud_yaxis, na.rm=TRUE)[1] 0.0302521sd(study1_w_type$rating_cl_raincloud_yaxis, na.rm=TRUE)[1] 0.3805782# u3
mean(study1_w_type$rating_u3_gardneraltman_xaxis, na.rm=TRUE)[1] 26.5641sd(study1_w_type$rating_u3_gardneraltman_xaxis, na.rm=TRUE)[1] 21.60994mean(study1_w_type$rating_u3_halfeye_xaxis, na.rm=TRUE)[1] 26.73016sd(study1_w_type$rating_u3_halfeye_xaxis, na.rm=TRUE)[1] 23.01318mean(study1_w_type$rating_u3_halfeye_yaxis, na.rm=TRUE)[1] 27.08475sd(study1_w_type$rating_u3_halfeye_yaxis, na.rm=TRUE)[1] 23.33354mean(study1_w_type$rating_u3_raincloud_yaxis, na.rm=TRUE)[1] 24.27731sd(study1_w_type$rating_u3_raincloud_yaxis, na.rm=TRUE)[1] 22.53657# overlap
mean(study1_w_type$rating_ov_gardneraltman_xaxis, na.rm=TRUE)[1] 68.63248sd(study1_w_type$rating_ov_gardneraltman_xaxis, na.rm=TRUE)[1] 30.38648mean(study1_w_type$rating_ov_halfeye_xaxis, na.rm=TRUE)[1] 74.73016sd(study1_w_type$rating_ov_halfeye_xaxis, na.rm=TRUE)[1] 26.46217mean(study1_w_type$rating_ov_halfeye_yaxis, na.rm=TRUE)[1] 70.07627sd(study1_w_type$rating_ov_halfeye_yaxis, na.rm=TRUE)[1] 30.92095mean(study1_w_type$rating_ov_raincloud_yaxis, na.rm=TRUE)[1] 72.83193sd(study1_w_type$rating_ov_raincloud_yaxis, na.rm=TRUE)[1] 26.56693# create a list of u3_misconceptualizers
u3_misconceptualizers <-
study1_w %>%
filter(rating_u3_missconcept == T) %>%
pull(session) %>%
unique()
# create a list of ov_misconceptualizers
ov_misconceptualizers <-
study1_w %>%
filter(rating_ov_missconcept == T) %>%
pull(session) %>%
unique()
### wrangle time stamp data ####################################################
study1_w_timestamp <-
read_csv(here("data/teachers_study1_N40_detailed.csv")) %>%
# filter participants from study1_w only
filter(session %in% study1_w$session) %>%
# we only need vars sensitivity or accuracy
dplyr::filter(str_detect(item_name, "sensi|acccl|accu3|accov")) %>%
# create var with plot number
mutate(plot = paste0("plotx_", str_sub(item_name, -2, -1)),
# recode wrong item labelling
plot = ifelse(plot == "plotx__6", "plotx_06", plot)) %>%
relocate(session, plot) %>%
# delete the page number in item name
mutate(item_name = str_sub(item_name, 1, 5)) %>%
pivot_wider(id_cols = c(session, plot), names_from = item_name,
values_from = answered_relative) %>%
rowwise() %>%
# what was the time of the first item to be clicked?
mutate(effic = min(sensi, acccl, accu3, accov, na.rm=T)) %>%
ungroup() %>%
dplyr::select(session, plot, effic, sensi, acccl, accu3, accov) %>%
left_join(., study1_w %>%
select(session, plot, type), by=c("session", "plot")) %>%
# generate data set so that the six plots from the same type are ordered
# one after the other (and not 1-24)
group_by(session, type) %>%
arrange(plot) %>%
mutate(plotNrWithin = 1:n()) %>%
ungroup() %>%
group_by(plotNrWithin, type) %>%
mutate(effic_10righttrunc = ifelse(effic > quantile(effic, .9), NA, effic),
effic_05righttrunc = ifelse(effic > quantile(effic, .95), NA, effic),
log_effic_05righttrunc =log(effic_05righttrunc),
log_effic_10righttrunc = log(effic_10righttrunc),
plotNrWithin0 = plotNrWithin -1,
plotNrWithin_factor = as.factor(plotNrWithin)) %>%
ungroup()
skim(study1_w_timestamp)| Name | study1_w_timestamp |
| Number of rows | 960 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| factor | 1 |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
| plot | 0 | 1 | 8 | 8 | 0 | 24 | 0 |
| type | 0 | 1 | 13 | 19 | 0 | 4 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| plotNrWithin_factor | 0 | 1 | FALSE | 6 | 1: 160, 2: 160, 3: 160, 4: 160 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| effic | 0 | 1.00 | 28817.81 | 67778.16 | 2419.00 | 9065.93 | 14866.60 | 26444.00 | 1342595.60 | ▇▁▁▁▁ |
| sensi | 480 | 0.50 | 22664.69 | 53591.36 | 2419.00 | 7670.72 | 11408.65 | 20709.33 | 684967.00 | ▇▁▁▁▁ |
| acccl | 557 | 0.42 | 33590.02 | 83104.51 | 3547.30 | 11650.00 | 18116.60 | 32103.65 | 1349401.70 | ▇▁▁▁▁ |
| accu3 | 480 | 0.50 | 61388.83 | 88359.00 | 5599.30 | 22174.12 | 39723.25 | 70041.55 | 1342595.60 | ▇▁▁▁▁ |
| accov | 480 | 0.50 | 69364.58 | 90056.87 | 6901.30 | 26939.18 | 46882.25 | 79074.25 | 1344927.30 | ▇▁▁▁▁ |
| plotNrWithin | 0 | 1.00 | 3.50 | 1.71 | 1.00 | 2.00 | 3.50 | 5.00 | 6.00 | ▇▃▃▃▃ |
| effic_10righttrunc | 96 | 0.90 | 16914.40 | 11789.22 | 2419.00 | 8734.20 | 13402.60 | 21572.35 | 89854.90 | ▇▂▁▁▁ |
| effic_05righttrunc | 48 | 0.95 | 19435.38 | 17815.29 | 2419.00 | 8910.87 | 14118.35 | 23782.15 | 168247.90 | ▇▁▁▁▁ |
| log_effic_05righttrunc | 48 | 0.95 | 9.60 | 0.71 | 7.79 | 9.10 | 9.56 | 10.08 | 12.03 | ▂▇▇▃▁ |
| log_effic_10righttrunc | 96 | 0.90 | 9.53 | 0.64 | 7.79 | 9.08 | 9.50 | 9.98 | 11.41 | ▁▆▇▅▁ |
| plotNrWithin0 | 0 | 1.00 | 2.50 | 1.71 | 0.00 | 1.00 | 2.50 | 4.00 | 5.00 | ▇▃▃▃▃ |
mean(study1_w_timestamp$effic)[1] 28817.81sd(study1_w_timestamp$effic)[1] 67778.16study1_w_timestamp_type <- study1_w_timestamp %>%
select(sensi, effic, session, type, plot) %>%
gather(var, value, sensi, effic)%>%
mutate(variable = paste(var, type, sep = "_"))%>%
select(-type, -var)%>%
group_by(session, plot) %>%
spread(variable, value) %>%
ungroup()
skim(study1_w_timestamp_type)| Name | study1_w_timestamp_type |
| Number of rows | 960 |
| Number of columns | 10 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 8 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
| plot | 0 | 1 | 8 | 8 | 0 | 24 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| effic_gardneraltman_xaxis | 720 | 0.25 | 35739.25 | 99037.01 | 3547.3 | 10743.50 | 17592.75 | 32584.00 | 1342595.6 | ▇▁▁▁▁ |
| effic_halfeye_xaxis | 720 | 0.25 | 27179.72 | 62047.66 | 2897.0 | 8788.35 | 13740.70 | 23012.73 | 684967.0 | ▇▁▁▁▁ |
| effic_halfeye_yaxis | 720 | 0.25 | 23954.41 | 44387.89 | 2419.0 | 8960.30 | 13016.15 | 23628.10 | 466834.3 | ▇▁▁▁▁ |
| effic_raincloud_yaxis | 720 | 0.25 | 28397.86 | 52248.67 | 2769.0 | 8910.65 | 14126.85 | 25963.05 | 598871.6 | ▇▁▁▁▁ |
| sensi_gardneraltman_xaxis | 837 | 0.13 | 20619.07 | 20690.87 | 4326.0 | 8835.95 | 14150.30 | 24945.10 | 140178.2 | ▇▁▁▁▁ |
| sensi_halfeye_xaxis | 846 | 0.12 | 23312.30 | 67326.04 | 2897.0 | 7717.35 | 11111.55 | 17859.72 | 684967.0 | ▇▁▁▁▁ |
| sensi_halfeye_yaxis | 838 | 0.13 | 21960.91 | 56691.29 | 2419.0 | 6957.90 | 9894.00 | 17751.38 | 466834.3 | ▇▁▁▁▁ |
| sensi_raincloud_yaxis | 839 | 0.13 | 24843.57 | 59244.50 | 2769.0 | 6906.80 | 11178.70 | 21190.10 | 598871.6 | ▇▁▁▁▁ |
# efficacy
mean(study1_w_timestamp_type$effic_gardneraltman_xaxis, na.rm=TRUE)[1] 35739.25sd(study1_w_timestamp_type$effic_gardneraltman_xaxis, na.rm=TRUE)[1] 99037.01mean(study1_w_timestamp_type$effic_halfeye_xaxis, na.rm=TRUE)[1] 27179.72sd(study1_w_timestamp_type$effic_halfeye_xaxis, na.rm=TRUE)[1] 62047.66mean(study1_w_timestamp_type$effic_halfeye_yaxis, na.rm=TRUE)[1] 23954.41sd(study1_w_timestamp_type$effic_halfeye_yaxis, na.rm=TRUE)[1] 44387.89mean(study1_w_timestamp_type$effic_raincloud_yaxis, na.rm=TRUE)[1] 28397.86sd(study1_w_timestamp_type$effic_raincloud_yaxis, na.rm=TRUE)[1] 52248.67study1_w %>%
select(rating_cl, rating_u3, rating_ov) %>%
pivot_longer(
c(rating_cl, rating_u3, rating_ov),
names_to = "dependent_variable",
values_to = "rated_effectsize"
) %>%
ggplot(., aes(rated_effectsize)) +
geom_histogram() +
xlab("Rated effect size") +
facet_wrap(~dependent_variable, scales = "free_x",
labeller = as_labeller(c("rating_cl" = "Abstract metric",
"rating_ov" = "Overlap metric",
"rating_u3" = "Cohen's U3 metric"))) +
theme_modern_rc() +
theme(strip.text = element_text(color = "white"))
Somewhat disturbing is the first mode in rating_ov. Maybe some users confused overlap and non-overlap? Another artifact seems to be the first mode in rating_u3.
study1_w_demo %>%
ggplot(., aes(effsize, rating_cl, color = as.factor(schooltype_binary))) +
geom_jitter() +
stat_smooth() +
ylab("Abstract metric") +
xlab("Plotted effect size") +
labs(color = "Schooltype") +
theme_modern_rc()
study1_w_demo%>%
ggplot(., aes(effsize, rating_u3, color = as.factor(schooltype_binary))) +
geom_jitter() +
stat_smooth() +
ylab("Cohen's U3 metric") +
xlab("Plotted effect size") +
labs(color = "Schooltype") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(abs(effsize), rating_ov, color = as.factor(schooltype_binary))) +
geom_jitter() +
stat_smooth(method = "lm") +
ylab("Overlap metric") +
xlab("Plotted effect size") +
labs(color = "Schooltype") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(as.factor(schooltype_binary), infor)) +
geom_jitter()+
stat_summary(fun.data = mean_sdl,
fun.args = list(mult =1),
color = "white") +
ylab("Perceived Informativity") +
xlab("School Type 1") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(as.factor(schooltype_binary), diffi)) +
geom_jitter()+
stat_summary(fun.data = mean_sdl,
fun.args = list(mult =1),
color = "white") +
ylab("Perceived Difficulty") +
xlab("School Type") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(as.factor(schooltype_binary), value)) +
geom_jitter()+
stat_summary(fun.data = mean_sdl,
fun.args = list(mult =1),
color = "white") +
ylab("Perceived Value") +
xlab("School Type 1=others") +
theme_modern_rc()
rating_cl, rating_u3 and rating_ov## accuracy U3
### overall
study1_w_demo %>%
do(tau_u3 = unlist(cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3)# A tibble: 1 × 1
tau_u3
<dbl>
1 -0.0837### grouped by plot type
study1_w_demo %>%
group_by(type) %>%
do(tau_u3 = unlist(cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3)# A tibble: 4 × 2
type tau_u3
<chr> <dbl>
1 gardneraltman_xaxis -0.0334
2 halfeye_xaxis -0.143
3 halfeye_yaxis -0.0423
4 raincloud_yaxis -0.118 ### grouped by school type
study1_w_demo %>%
group_by(schooltype_binary) %>%
do(tau_u3a = unlist(cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3a)# A tibble: 2 × 2
schooltype_binary tau_u3a
<dbl> <dbl>
1 0 -0.0649
2 1 -0.122 ### grouped by school type and plot type
study1_w_demo %>%
group_by(schooltype_binary, type) %>%
do(tau_u3 = unlist(cor(abs(.$effsize), .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3)# A tibble: 8 × 3
schooltype_binary type tau_u3
<dbl> <chr> <dbl>
1 0 gardneraltman_xaxis 0.105
2 0 halfeye_xaxis 0.188
3 0 halfeye_yaxis 0.210
4 0 raincloud_yaxis 0.0956
5 1 gardneraltman_xaxis 0.0805
6 1 halfeye_xaxis 0.131
7 1 halfeye_yaxis 0.0252
8 1 raincloud_yaxis 0.133 ## overlap
### overall
study1_w_demo %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 1 × 1
tau_ov
<dbl>
1 -0.276### grouped by plot type
study1_w_demo %>%
group_by(type) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 4 × 2
type tau_ov
<chr> <dbl>
1 gardneraltman_xaxis -0.188
2 halfeye_xaxis -0.228
3 halfeye_yaxis -0.326
4 raincloud_yaxis -0.348### grouped by school type
study1_w_demo %>%
group_by(schooltype_binary) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 2 × 2
schooltype_binary tau_ov
<dbl> <dbl>
1 0 -0.233
2 1 -0.345### grouped by school type and plot type
study1_w_demo %>%
group_by(schooltype_binary, type) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 8 × 3
schooltype_binary type tau_ov
<dbl> <chr> <dbl>
1 0 gardneraltman_xaxis -0.201
2 0 halfeye_xaxis -0.105
3 0 halfeye_yaxis -0.195
4 0 raincloud_yaxis -0.410
5 1 gardneraltman_xaxis -0.155
6 1 halfeye_xaxis -0.434
7 1 halfeye_yaxis -0.541
8 1 raincloud_yaxis -0.248## Abstract Metric
### overall
study1_w_demo %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 1 × 1
tau_cl
<dbl>
1 0.449### grouped by plot type
study1_w_demo %>%
group_by(type) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 4 × 2
type tau_cl
<chr> <dbl>
1 gardneraltman_xaxis 0.373
2 halfeye_xaxis 0.548
3 halfeye_yaxis 0.484
4 raincloud_yaxis 0.415### grouped by school type
study1_w_demo %>%
group_by(schooltype_binary) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 2 × 2
schooltype_binary tau_cl
<dbl> <dbl>
1 0 0.373
2 1 0.592### grouped by school type and plot type
study1_w_demo %>%
group_by(schooltype_binary, type) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 8 × 3
schooltype_binary type tau_cl
<dbl> <chr> <dbl>
1 0 gardneraltman_xaxis 0.379
2 0 halfeye_xaxis 0.442
3 0 halfeye_yaxis 0.421
4 0 raincloud_yaxis 0.280
5 1 gardneraltman_xaxis 0.371
6 1 halfeye_xaxis 0.699
7 1 halfeye_yaxis 0.590
8 1 raincloud_yaxis 0.701study1_w_demo_vda <- study1_w_demo %>%
select(session, plot, rating_ov, rating_cl, rating_u3, schooltype_binary) %>%
pivot_longer(rating_ov:rating_u3, names_to = "variables",
values_to = "values") %>%
mutate(var = paste(variables, schooltype_binary, sep = "_")) %>%
select(-variables, values) %>%
group_by(session, plot) %>%
pivot_wider(names_from = "var", values_from = "values")
effsize::VD.A(na.omit(study1_w_demo_vda$rating_cl_1), na.omit(study1_w_demo_vda$rating_cl_0))
Vargha and Delaney A
A estimate: 0.534 (negligible)effsize::VD.A(na.omit(study1_w_demo_vda$rating_u3_1), na.omit(study1_w_demo_vda$rating_u3_0))
Vargha and Delaney A
A estimate: 0.5272222 (negligible)effsize::VD.A(na.omit(study1_w_demo_vda$rating_ov_1), na.omit(study1_w_demo_vda$rating_ov_0))
Vargha and Delaney A
A estimate: 0.5908981 (small)ggplot(study1_w %>%
select(rating_cl, rating_u3, rating_ov, effsize, effsize_cl, session) %>%
pivot_longer(
c(rating_cl, rating_u3, rating_ov),
names_to = "operationalization",
values_to = "judged_effectsize"
),
aes(judged_effectsize, as.numeric(as.factor(session)),
color = session)
) +
geom_jitter(height = 0) +
xlab("Rated effect size") +
ylab("Participant ID") +
facet_wrap(~ operationalization, scales = "free_x",
labeller = as_labeller(c("rating_cl" = "Abstract metric",
"rating_ov" = "Overlap metric",
"rating_u3" = "Cohen's U3 metric"))) +
theme_modern_rc() +
theme(legend.position = "none",
strip.text = element_text(color = "white"))
study1_w %>%
ggplot(., aes(effsize, rating_cl)) +
geom_jitter() +
stat_smooth() +
ylab("Abstract metric") +
xlab("Plotted effect size") +
theme_modern_rc()
study1_w %>%
ggplot(., aes(effsize, rating_u3, color = rating_u3_missconcept)) +
geom_jitter() +
stat_smooth() +
ylab("Cohen's U3 metric") +
xlab("Plotted effect size") +
labs(color = "Misconcept") +
theme_modern_rc()
study1_w %>%
ggplot(., aes(abs(effsize), rating_ov, color = rating_ov_missconcept)) +
geom_jitter() +
stat_smooth(method = "lm") +
ylab("Overlap metric") +
xlab("Plotted effect size") +
labs(color = "Misconcept") +
theme_modern_rc()
The correlations underpin the interpretation of the misconceptions. Therefore, we will look at the intercorrelations of the dependent variables.
rating_cl, rating_u3 and rating_ovstudy1_w %>%
group_by(type) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 4 × 2
type tau_cl
<chr> <dbl>
1 gardneraltman_xaxis 0.373
2 halfeye_xaxis 0.548
3 halfeye_yaxis 0.484
4 raincloud_yaxis 0.415study1_w %>%
group_by(rating_ov_missconcept, type) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 8 × 3
rating_ov_missconcept type tau_ov
<lgl> <chr> <dbl>
1 FALSE gardneraltman_xaxis -0.355
2 FALSE halfeye_xaxis -0.384
3 FALSE halfeye_yaxis -0.478
4 FALSE raincloud_yaxis -0.523
5 TRUE gardneraltman_xaxis 0.0236
6 TRUE halfeye_xaxis 0.119
7 TRUE halfeye_yaxis 0.0205
8 TRUE raincloud_yaxis -0.220 study1_w %>%
group_by(rating_u3_missconcept, type) %>%
do(tau_u3 = cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete")) %>%
unnest(tau_u3)# A tibble: 8 × 3
rating_u3_missconcept type tau_u3
<lgl> <chr> <dbl>
1 FALSE gardneraltman_xaxis -0.298
2 FALSE halfeye_xaxis -0.471
3 FALSE halfeye_yaxis -0.263
4 FALSE raincloud_yaxis -0.321
5 TRUE gardneraltman_xaxis 0.119
6 TRUE halfeye_xaxis -0.153
7 TRUE halfeye_yaxis -0.108
8 TRUE raincloud_yaxis -0.0971u3_misconcept_schooltype <- study1_w_demo %>%
select(session, rating_u3_missconcept, schooltype_binary) %>%
unique()
count(u3_misconcept_schooltype, rating_u3_missconcept, schooltype_binary)# A tibble: 4 × 3
rating_u3_missconcept schooltype_binary n
<lgl> <dbl> <int>
1 FALSE 0 11
2 FALSE 1 7
3 TRUE 0 14
4 TRUE 1 8ov_misconcept_schooltype <- study1_w_demo %>%
select(session, rating_ov_missconcept, schooltype_binary) %>%
unique()
count(ov_misconcept_schooltype, rating_ov_missconcept, schooltype_binary)# A tibble: 4 × 3
rating_ov_missconcept schooltype_binary n
<lgl> <dbl> <int>
1 FALSE 0 18
2 FALSE 1 13
3 TRUE 0 7
4 TRUE 1 2ggplot(study1_w,
aes(rating_cl, rating_u3)) +
geom_jitter() +
xlab("Abstract metric") +
ylab("Cohen's U3 metric") +
theme_modern_rc() +
ggplot(study1_w,
aes(rating_cl, rating_ov)) +
geom_jitter() +
xlab("Abstract metric") +
ylab("Overlap metric") +
theme_modern_rc() +
ggplot(study1_w,
aes(rating_ov, rating_u3)) +
geom_jitter() +
xlab("Overlap metric") +
ylab("Cohen's U3 metric") +
theme_modern_rc()
study1_w %>%
select(diff_cl, diff_u3_filtered, diff_ov_filtered, session, type) %>%
gather(dependent_variable, difference_to_true_effsize,
diff_cl, diff_u3_filtered, diff_ov_filtered) %>%
ggplot(., aes(type, difference_to_true_effsize)) +
geom_boxplot(alpha = .3) +
facet_wrap(~dependent_variable, scales = "free_y",
labeller = as_labeller(c("diff_cl" = "Abstract metric",
"diff_ov_filtered" = "Overlap metric (filtered)",
"diff_u3_filtered" = "Cohen's U3 metric (filtered)"))) +
geom_jitter(aes(color = type)) +
ylab("Difference to true effect size") +
xlab("Accuracy item") +
labs(color = "Accuracy item") +
theme_modern_rc() +
theme(strip.text = element_text(color = "white"),
axis.text.x = element_blank())
#| results: hide
# Rating abstract metric
mod0_rating_cl <- brm(rating_cl ~ + (1|session),
data = study1_w,
iter = 20000,
save_pars = save_pars(all = TRUE),
cores = 4)
mod1_rating_cl <- brm(rating_cl ~ effsize + (1|session),
data = study1_w,
iter = 20000,
save_pars = save_pars(all = TRUE),
cores = 4)
mod2_rating_cl <- brm(rating_cl ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_rating_cl, mod0_rating_cl)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_rating_cl over mod0_rating_cl: 694770923350984417317368128077824.00000bayes_factor(mod2_rating_cl, mod1_rating_cl)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_rating_cl over mod1_rating_cl: 0.00113#| results: hide
# Rating Cohen's U3
mod0_rating_u3 <- brm(rating_u3 ~ + (1|session),
data = study1_w%>%
filter(rating_u3_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_rating_u3 <- brm(rating_u3 ~ effsize + (1|session),
data = study1_w%>%
filter(rating_u3_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_rating_u3 <- brm(rating_u3 ~ type + effsize + (1|session),
data = study1_w %>%
filter(rating_u3_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_rating_u3, mod0_rating_u3)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_rating_u3 over mod0_rating_u3: 1051568871.23350bayes_factor(mod2_rating_u3, mod1_rating_u3)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of mod2_rating_u3 over mod1_rating_u3: 2398.20432#| results: hide
# Rating Overlap
mod0_rating_ov <- brm(rating_ov ~ + (1|session),
data = study1_w%>%
filter(rating_ov_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_rating_ov <- brm(rating_ov ~ effsize + (1|session),
data = study1_w%>%
filter(rating_ov_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_rating_ov <- brm(rating_ov ~ type + effsize + (1|session),
data = study1_w %>%
filter(rating_ov_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_rating_ov, mod0_rating_ov)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_rating_ov over mod0_rating_ov: 4.80568bayes_factor(mod2_rating_ov, mod1_rating_ov)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_rating_ov over mod1_rating_ov: 538.28196study1_w %>%
ggplot(aes(type, diffi)) +
geom_jitter() +
stat_summary(fun.data = mean_sdl,
fun.args = list(mult = 1),
color = "white") +
theme_modern_rc() +
labs(title = "Difficulty",
subtitle = "per Plot Type",
caption = "Means ± 1*SD")
# Perceived Difficulty
mod0_diffi <- brm(diffi ~ + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_diffi <- brm(diffi ~ effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_diffi <- brm(diffi ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_diffi, mod0_diffi)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of mod1_diffi over mod0_diffi: 0.39498bayes_factor(mod2_diffi, mod1_diffi)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_diffi over mod1_diffi: 4874251106763547.00000study1_w %>%
ggplot(aes(type, infor)) +
geom_jitter() +
stat_summary(fun.data = mean_sdl,
fun.args = list(mult = 1),
color = "white") +
theme_modern_rc() +
labs(title = "Informativity",
subtitle = "per Plot Type",
caption = "Means ± 1*SD")
# Perceived Informativity
mod0_infor <- brm(infor ~ + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_infor <- brm(infor ~ effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_infor <- brm(infor ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_infor, mod0_infor)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_infor over mod0_infor: 0.25868bayes_factor(mod2_infor, mod1_infor)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_infor over mod1_infor: 727215.56159study1_w %>%
ggplot(aes(type, value)) +
geom_jitter() +
stat_summary(fun.data = mean_sdl,
fun.args = list(mult = 1),
color = "white") +
theme_modern_rc() +
labs(title = "Perceived Value",
subtitle = "per Plot Type",
caption = "Means ± 1*SD")
# Perceived Value
mod0_value <- brm(value ~ + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_value <- brm(value ~ effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_value <- brm(value ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_value, mod0_value)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_value over mod0_value: 0.21894bayes_factor(mod2_value, mod1_value)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_value over mod1_value: 84034532.38619sjPlot::tab_model(
mod0_rating_cl, mod0_rating_u3, mod0_rating_ov,
mod0_diffi, mod0_infor, mod0_value,
mod1_rating_cl, mod1_rating_u3, mod1_rating_ov,
mod1_diffi, mod1_infor, mod1_value,
mod2_rating_cl, mod2_rating_u3, mod2_rating_ov,
mod2_diffi, mod2_infor, mod2_value
)| rating_cl | rating_u3 | rating_ov | diffi | infor | value | rating_cl | rating_u3 | rating_ov | diffi | infor | value | rating_cl | rating_u3 | rating_ov | diffi | infor | value | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 0.02 | -0.02 – 0.05 | 43.09 | 38.48 – 47.78 | 82.69 | 80.18 – 85.26 | 4.05 | 3.68 – 4.40 | 4.11 | 3.81 – 4.40 | 4.13 | 3.79 – 4.47 | 0.01 | -0.02 – 0.05 | 43.34 | 38.71 – 48.00 | 82.70 | 80.19 – 85.22 | 4.05 | 3.68 – 4.42 | 4.11 | 3.81 – 4.40 | 4.13 | 3.80 – 4.47 | 0.01 | -0.05 – 0.07 | 42.71 | 36.85 – 48.44 | 82.30 | 78.50 – 86.23 | 3.59 | 3.18 – 3.97 | 3.80 | 3.48 – 4.14 | 3.79 | 3.42 – 4.13 |
| effsize | 0.37 | 0.32 – 0.42 | -11.79 | -15.33 – -8.19 | -1.04 | -4.02 – 2.00 | -0.09 | -0.24 – 0.06 | -0.06 | -0.19 – 0.07 | -0.05 | -0.17 – 0.08 | 0.37 | 0.32 – 0.42 | -11.62 | -15.16 – -8.10 | -1.09 | -4.11 – 1.92 | -0.09 | -0.23 – 0.05 | -0.06 | -0.19 – 0.07 | -0.05 | -0.17 – 0.07 | ||||||||||||
| typehalfeye_xaxis | -0.02 | -0.10 – 0.06 | 0.54 | -5.07 – 6.19 | 2.46 | -2.22 – 7.16 | 0.65 | 0.42 – 0.87 | 0.39 | 0.19 – 0.59 | 0.51 | 0.32 – 0.70 | ||||||||||||||||||||||||
| typehalfeye_yaxis | 0.01 | -0.08 – 0.09 | 4.10 | -1.62 – 9.81 | -0.98 | -5.78 – 3.77 | 0.97 | 0.74 – 1.19 | 0.61 | 0.40 – 0.81 | 0.58 | 0.39 – 0.77 | ||||||||||||||||||||||||
| typeraincloud_yaxis | 0.01 | -0.08 – 0.09 | -1.85 | -7.62 – 3.90 | -0.14 | -4.90 – 4.76 | 0.24 | 0.02 – 0.47 | 0.24 | 0.04 – 0.44 | 0.23 | 0.04 – 0.42 | ||||||||||||||||||||||||
| Random Effects | ||||||||||||||||||||||||||||||||||||
| σ2 | 0.15 | 259.13 | 254.81 | 1.72 | 1.32 | 1.17 | 0.11 | 214.02 | 255.24 | 1.72 | 1.32 | 1.17 | 0.11 | 213.00 | 256.19 | 1.58 | 1.27 | 1.11 | ||||||||||||||||||
| τ00 | 0.00 session | 75.29 session | 29.95 session | 1.31 session | 0.87 session | 1.15 session | 0.00 session | 82.64 session | 29.80 session | 1.31 session | 0.87 session | 1.14 session | 0.00 session | 79.60 session | 29.09 session | 1.32 session | 0.87 session | 1.14 session | ||||||||||||||||||
| ICC | 0.01 | 0.23 | 0.11 | 0.43 | 0.40 | 0.50 | 0.02 | 0.28 | 0.10 | 0.43 | 0.40 | 0.49 | 0.02 | 0.27 | 0.10 | 0.46 | 0.41 | 0.51 | ||||||||||||||||||
| N | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | ||||||||||||||||||
| Observations | 480 | 216 | 372 | 960 | 960 | 960 | 480 | 216 | 372 | 960 | 960 | 960 | 480 | 216 | 372 | 960 | 960 | 960 | ||||||||||||||||||
| Marginal R2 / Conditional R2 | 0.000 / 0.006 | 0.000 / 0.198 | 0.000 / 0.098 | 0.000 / 0.413 | 0.000 / 0.376 | 0.000 / 0.475 | 0.279 / 0.291 | 0.134 / 0.343 | 0.002 / 0.101 | 0.001 / 0.414 | 0.001 / 0.377 | 0.000 / 0.476 | 0.282 / 0.294 | 0.154 / 0.359 | 0.015 / 0.112 | 0.050 / 0.463 | 0.025 / 0.401 | 0.026 / 0.501 | ||||||||||||||||||
ggplot(study1_w %>%
select(sensi_ordinal, type) %>%
na.omit(), aes(sensi_ordinal,
color = sensi_ordinal,
fill = sensi_ordinal)) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white")) +
ggtitle("Sensitivity", "all participants")
ggplot(study1_w %>%
select(sensi_ordinal, type, session) %>%
na.omit() %>%
filter(!session %in% ov_misconceptualizers &
!session %in% u3_misconceptualizers), aes(sensi_ordinal,
color = sensi_ordinal,
fill = sensi_ordinal)) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white")) +
ggtitle("Sensitivity", "without missconceptualizers")
ggplot(study1_w %>%
select(sensi_correct, type, session) %>%
dplyr::filter(!is.na(sensi_correct)),
aes(sensi_correct,
color = sensi_correct,
fill = sensi_correct)) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(color = "", fill = "") +
ggtitle("Binary Sensitivity", "without wrong decisions")
ggplot(study1_w %>%
select(sensi_binary_filtered, type, session),
aes(sensi_binary_filtered,
color = as.factor(sensi_binary_filtered),
fill = as.factor(sensi_binary_filtered))) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white")) +
labs(color = "", fill = "") +
ggtitle("Binary Sensitivity", "without wrong decisions (0=equal, 1=right direction)")
Fitting a series of logistic regressions with non-informative priors
#| results: hide
logreg_mod0 <- brm(
sensi_binary_filtered ~ +(1 | session),
family = bernoulli(link = "logit"),
data = study1_w,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4
)
logreg_mod1 <-
brm(
sensi_binary_filtered ~ effsize_abs + (1 | session),
family = bernoulli(link = "logit"),
data = study1_w,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4
)
logreg_mod2 <-
brm(
sensi_binary_filtered ~ effsize_abs + type + (1 | session),
family = bernoulli(link = "logit"),
data = study1_w,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4
)bayes_factor(logreg_mod1, logreg_mod0)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 7
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of logreg_mod1 over logreg_mod0: 435182901565522720645523177472.00000bayes_factor(logreg_mod2, logreg_mod1)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of logreg_mod2 over logreg_mod1: 15.67848sjPlot::tab_model(logreg_mod0, logreg_mod1, logreg_mod2, show.icc = TRUE)| sensi_binary_filtered | sensi_binary_filtered | sensi_binary_filtered | ||||
|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI (95%) | Odds Ratios | CI (95%) | Odds Ratios | CI (95%) |
| Intercept | 3.08 | 1.56 – 6.31 | 0.11 | 0.03 – 0.34 | 0.08 | 0.02 – 0.34 |
| effsize_abs | 2852.08 | 509.02 – 19973.65 | 3497.57 | 574.60 – 26363.85 | ||
| typehalfeye_xaxis | 1.92 | 0.73 – 5.06 | ||||
| typehalfeye_yaxis | 1.84 | 0.73 – 4.84 | ||||
| typeraincloud_yaxis | 0.77 | 0.31 – 1.90 | ||||
| Random Effects | ||||||
| σ2 | 3.29 | 3.29 | 3.29 | |||
| τ00 | 3.85 session | 10.23 session | 11.27 session | |||
| ICC | 0.54 | 0.76 | 0.77 | |||
| N | 40 session | 40 session | 40 session | |||
| Observations | 453 | 453 | 453 | |||
| Marginal R2 / Conditional R2 | 0.000 / 0.318 | 0.299 / 0.597 | 0.306 / 0.605 | |||
pp_check(logreg_mod2) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
ggplot(study1_w_timestamp, aes(as.factor(plotNrWithin), effic)) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
coord_cartesian(ylim = c(0,100000)) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
ggplot(study1_w_timestamp, aes(as.factor(plotNrWithin), effic_05righttrunc)) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
coord_cartesian(ylim = c(0,85000)) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="5% Truncated Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
ggplot(study1_w_timestamp, aes(as.factor(plotNrWithin), effic_10righttrunc)) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
coord_cartesian(ylim = c(0,85000)) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="10% Truncated Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
log() Transformedggplot(study1_w_timestamp,
aes(as.factor(plotNrWithin),
log(effic_05righttrunc))) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="log Transformed Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
{brms}#| error: false
#| warning: false
#| results: hide
study1_w_timestamp_effsize <- study1_w_timestamp %>%
full_join(study1_w %>% select(session, effsize), by = "session")
plot01_mod0 <-
brm(log_effic_05righttrunc ~ 1 + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot01_mod0) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot01_mod1 <-
brm(log_effic_05righttrunc~ 1 + effsize + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot01_mod1) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot01_mod2 <-
brm(log_effic_05righttrunc ~ 1 + effsize + type + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot01_mod2) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
sjPlot::tab_model(plot01_mod0, plot01_mod1, plot01_mod2)| log_effic_05righttrunc | log_effic_05righttrunc | log_effic_05righttrunc | ||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 10.13 | 9.96 – 10.28 | 10.13 | 9.97 – 10.28 | 10.50 | 10.33 – 10.66 |
| effsize | -0.00 | -0.03 – 0.03 | 0.00 | -0.03 – 0.03 | ||
| typehalfeye_xaxis | -0.43 | -0.47 – -0.39 | ||||
| typehalfeye_yaxis | -0.54 | -0.58 – -0.49 | ||||
| typeraincloud_yaxis | -0.50 | -0.55 – -0.46 | ||||
| Random Effects | ||||||
| σ2 | 0.25 | 0.25 | 0.20 | |||
| τ00 | 0.24 session | 0.25 session | 0.25 session | |||
| ICC | 0.49 | 0.49 | 0.55 | |||
| N | 40 session | 40 session | 40 session | |||
| Observations | 3648 | 3648 | 3648 | |||
| Marginal R2 / Conditional R2 | 0.000 / 0.458 | 0.000 / 0.458 | 0.100 / 0.558 | |||
bayes_factor(plot01_mod1, plot01_mod0)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of plot01_mod1 over plot01_mod0: 0.03892bayes_factor(plot01_mod2, plot01_mod1)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 7
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of plot01_mod2 over plot01_mod1: 1770776011793288578099518651780334949935988681027090403823789893788615439226156721325265870403861352189977128553873231777644561722392387052732500347386331136.00000#| error: false
#| warning: false
#| results: hide
plot0306_mod0 <-
brm(log_effic_05righttrunc ~ 1 + plotNrWithin_factor + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot0306_mod0) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot0306_mod1 <-
brm(log_effic_05righttrunc ~ 1 + plotNrWithin_factor + effsize +
(1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
pp_check(plot0306_mod1) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot0306_mod2 <-
brm(log_effic_05righttrunc ~ 1 + plotNrWithin_factor + effsize +
type + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
pp_check(plot0306_mod2) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
sjPlot::tab_model(plot0306_mod0, plot0306_mod1, plot0306_mod2)| log_effic_05righttrunc | log_effic_05righttrunc | log_effic_05righttrunc | ||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 9.48 | 9.35 – 9.61 | 9.48 | 9.35 – 9.62 | 9.59 | 9.46 – 9.72 |
| plotNrWithin_factor: plotNrWithin_factor5 |
-0.01 | -0.03 – 0.02 | -0.01 | -0.03 – 0.01 | -0.01 | -0.03 – 0.01 |
| plotNrWithin_factor: plotNrWithin_factor6 |
-0.19 | -0.21 – -0.17 | -0.19 | -0.21 – -0.17 | -0.19 | -0.21 – -0.17 |
| effsize | 0.00 | -0.02 – 0.02 | 0.00 | -0.02 – 0.02 | ||
| typehalfeye_xaxis | -0.13 | -0.16 – -0.11 | ||||
| typehalfeye_yaxis | -0.21 | -0.23 – -0.18 | ||||
| typeraincloud_yaxis | -0.06 | -0.09 – -0.04 | ||||
| Random Effects | ||||||
| σ2 | 0.24 | 0.24 | 0.24 | |||
| τ00 | 0.17 session | 0.17 session | 0.17 session | |||
| ICC | 0.41 | 0.41 | 0.42 | |||
| N | 40 session | 40 session | 40 session | |||
| Observations | 10944 | 10944 | 10944 | |||
| Marginal R2 / Conditional R2 | 0.019 / 0.406 | 0.019 / 0.406 | 0.034 / 0.421 | |||
bayes_factor(plot0306_mod1, plot0306_mod0)Iteration: 1
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Iteration: 5Estimated Bayes factor in favor of plot0306_mod1 over plot0306_mod0: 0.02335bayes_factor(plot0306_mod2, plot0306_mod1)Iteration: 1
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Iteration: 6Estimated Bayes factor in favor of plot0306_mod2 over plot0306_mod1: 18416094205724295475395080047976396374990194327754375168.00000#| results: hide
# preparing multilevel model with brms for graphical overview
# rating u3
mod_plot_ratingu3 <- brm(
scale(rating_u3) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_ratingu3 <- mod_plot_ratingu3 %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean
#xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_text(hjust = 0)) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle("Accuracy in Cohen's U3 Metric")plot_ratingu3
#| results: hide
# rating ov
mod_plot_ratingov <- brm (
scale(rating_ov) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_ratingov <- mod_plot_ratingov %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean
#xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc()+
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Accuracy in Overlap Metric')plot_ratingov
#| results: hide
# difficulty
mod_plot_diffi <- brm (
scale(diffi) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_diffi <- mod_plot_diffi %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean
#xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Perceived Task Difficulty')plot_diffi
#| results: hide
# informativity
mod_plot_infor <- brm (
scale(infor) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_infor <- mod_plot_infor %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean #xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_text(hjust = 0)) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Perceived Informativity')plot_infor
#| results: hide
# value
mod_plot_value <- brm (
scale(value) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_value <- mod_plot_value %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean #xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Perceived Value')plot_value
# efficiency for the first plot
mod_plot_efficiency1 <-
brm(
scale(effic) ~ (1 | type),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
control = list(adapt_delta = .999),
cores = 4
)
plot_efficiency1 <- mod_plot_efficiency1 %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean #xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Efficiency for the First Plot')plot_efficiency1
# plot_contrasteffects <- plot_ratingu3 + plot_ratingov + plot_diffi + plot_infor + plot_value + plot_efficiency1 +
# plot_layout(ncol=3)
# ggsave("www/plot_contrasteffects.png",
# plot_contrasteffects,
# width = 25*.55,
# height = 16*.55){brms}# create dummy variables to compare visualization types for those measures that showed evidence for difference (accuracy U3, accuracy overlap, perceived task difficulty, perceived informativity, perceived value, efficiency)
## Gardner Altman Plot vs. Halfeye xaxis
gardneraltman_vs_halfeye_x <- study1_w %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_x =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_xaxis") %>%
ungroup()
gardneraltman_vs_halfeye_x_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_x =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_xaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")#| results: hide
mod_accu3_gardneraltman_halfeye_x <-
brm(scale(rating_u3) ~ dummy_gardneraltman_halfeye_x + effsize + (1|session),
data = gardneraltman_vs_halfeye_x%>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_accov_gardneraltman_halfeye_x <-
brm(scale(rating_ov) ~ dummy_gardneraltman_halfeye_x + effsize + (1|session),
data = gardneraltman_vs_halfeye_x%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)#| results: hide
mod_diffi_gardneraltman_halfeye_x <-
brm(scale(diffi) ~ dummy_gardneraltman_halfeye_x + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_infor_gardneraltman_halfeye_x <-
brm(scale(infor) ~ dummy_gardneraltman_halfeye_x + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)mod_value_gardneraltman_halfeye_x <-
brm(scale(value) ~ dummy_gardneraltman_halfeye_x + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_x,
save_pars = save_pars(all = TRUE))
mod_effic01_gardneraltman_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_x +
effsize + (1|session),
data = gardneraltman_vs_halfeye_x_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)mod_effic0306_gardneraltman_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_x +
effsize + (1|session),
data = gardneraltman_vs_halfeye_x_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
## Gardner Altman Plot vs. Halfeye yaxis
gardneraltman_vs_halfeye_y <- study1_w %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_y =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_yaxis") %>%
ungroup()gardneraltman_vs_halfeye_y_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_y =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")
mod_accu3_gardneraltman_halfeye_y <-
brm(scale(rating_u3) ~ dummy_gardneraltman_halfeye_y + effsize + (1|session),
data = gardneraltman_vs_halfeye_y%>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_accov_gardneraltman_halfeye_y <-
brm(scale(rating_ov) ~ dummy_gardneraltman_halfeye_y + effsize + (1|session),
data = gardneraltman_vs_halfeye_y%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_diffi_gardneraltman_halfeye_y <-
brm(scale(diffi) ~ dummy_gardneraltman_halfeye_y + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_infor_gardneraltman_halfeye_y <-
brm(scale(infor) ~ dummy_gardneraltman_halfeye_y + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_value_gardneraltman_halfeye_y <-
brm(scale(value) ~ dummy_gardneraltman_halfeye_y + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_effic01_gardneraltman_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_y +
effsize + (1|session),
data = gardneraltman_vs_halfeye_y_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic0306_gardneraltman_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_y +
effsize + (1|session),
data = gardneraltman_vs_halfeye_y_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)## Raincloud Plot vs. Halfeye x axis
raincloud_vs_halfeye_x <- study1_w %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_x =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_xaxis" | type == "raincloud_yaxis") %>%
ungroup()
raincloud_vs_halfeye_x_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_x =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "raincloud_yaxis" | type == "halfeye_xaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_raincloud_halfeye_x <-
brm(scale(rating_u3) ~ dummy_raincloud_halfeye_x + effsize + (1|session),
data = raincloud_vs_halfeye_x %>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_raincloud_halfeye_x <-
brm(scale(rating_ov) ~ dummy_raincloud_halfeye_x + effsize + (1|session),
data = raincloud_vs_halfeye_x %>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_diffi_raincloud_halfeye_x <-
brm(scale(diffi) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_infor_raincloud_halfeye_x <-
brm(scale(infor) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_raincloud_halfeye_x <-
brm(scale(value) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic01_raincloud_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)mod_effic0306_raincloud_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)## Raincloud Plot vs.Halfeye yaxis
raincloud_vs_halfeye_y <- study1_w %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_y =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_yaxis" | type == "raincloud_yaxis") %>%
ungroup()
raincloud_vs_halfeye_y_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_y =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "raincloud_yaxis" | type == "halfeye_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_raincloud_halfeye_y <-
brm(scale(rating_u3) ~ dummy_raincloud_halfeye_y + effsize + (1|session),
data = raincloud_vs_halfeye_y %>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_raincloud_halfeye_y <-
brm(scale(rating_ov) ~ dummy_raincloud_halfeye_y + effsize + (1|session),
data = raincloud_vs_halfeye_y %>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_diffi_raincloud_halfeye_y <-
brm(scale(diffi) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_infor_raincloud_halfeye_y <-
brm(scale(infor) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_raincloud_halfeye_y <-
brm(scale(value) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic01_raincloud_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_effic0306_raincloud_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)sjPlot::tab_model(mod_accu3_gardneraltman_halfeye_x,
mod_accov_gardneraltman_halfeye_x, mod_diffi_gardneraltman_halfeye_x,
mod_infor_gardneraltman_halfeye_x, mod_value_gardneraltman_halfeye_x,
mod_effic01_gardneraltman_halfeye_x, mod_effic0306_gardneraltman_halfeye_x,
mod_accu3_gardneraltman_halfeye_y, mod_accov_gardneraltman_halfeye_y,
mod_diffi_gardneraltman_halfeye_y, mod_infor_gardneraltman_halfeye_y,
mod_value_gardneraltman_halfeye_y,
mod_effic01_gardneraltman_halfeye_y, mod_effic0306_gardneraltman_halfeye_y,
mod_accov_raincloud_halfeye_x, mod_diffi_raincloud_halfeye_x,
mod_infor_raincloud_halfeye_x, mod_value_raincloud_halfeye_x,
mod_effic01_raincloud_halfeye_x, mod_effic0306_raincloud_halfeye_x,
mod_accu3_raincloud_halfeye_y, mod_accov_raincloud_halfeye_y,
mod_diffi_raincloud_halfeye_y, mod_infor_raincloud_halfeye_y,
mod_value_raincloud_halfeye_y,
mod_effic01_raincloud_halfeye_y, mod_effic0306_raincloud_halfeye_y)| scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | ||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | -0.01 | -0.34 – 0.34 | -0.11 | -0.34 – 0.12 | -0.19 | -0.44 – 0.07 | -0.13 | -0.39 – 0.12 | -0.17 | -0.43 – 0.09 | 0.33 | 0.09 – 0.58 | 0.10 | -0.10 – 0.31 | -0.17 | -0.51 – 0.17 | 0.01 | -0.24 – 0.26 | -0.27 | -0.51 – -0.03 | -0.20 | -0.43 – 0.04 | -0.19 | -0.41 – 0.05 | 0.43 | 0.17 – 0.67 | 0.17 | -0.03 – 0.37 | -0.11 | -0.33 – 0.09 | -0.13 | -0.36 – 0.12 | -0.05 | -0.27 – 0.17 | -0.09 | -0.34 – 0.16 | -0.05 | -0.35 – 0.23 | 0.07 | -0.16 – 0.29 | -0.16 | -0.47 – 0.15 | 0.03 | -0.19 – 0.27 | -0.23 | -0.45 – 0.01 | -0.12 | -0.35 – 0.10 | -0.12 | -0.36 – 0.12 | 0.05 | -0.22 – 0.33 | 0.14 | -0.09 – 0.37 |
| dummy_gardneraltman_halfeye_x | 0.04 | -0.28 – 0.35 | 0.18 | -0.11 – 0.46 | 0.37 | 0.25 – 0.49 | 0.26 | 0.14 – 0.39 | 0.35 | 0.24 – 0.46 | -0.69 | -0.75 – -0.64 | -0.20 | -0.24 – -0.16 | ||||||||||||||||||||||||||||||||||||||||
| effsize | -0.73 | -1.01 – -0.44 | 0.04 | -0.22 – 0.30 | -0.04 | -0.15 – 0.06 | -0.02 | -0.13 – 0.09 | -0.05 | -0.16 – 0.05 | 0.00 | -0.05 – 0.05 | 0.00 | -0.04 – 0.04 | -0.50 | -0.80 – -0.18 | -0.07 | -0.34 – 0.19 | -0.04 | -0.14 – 0.07 | -0.03 | -0.14 – 0.09 | -0.02 | -0.12 – 0.09 | -0.00 | -0.04 – 0.04 | 0.00 | -0.04 – 0.04 | -0.07 | -0.32 – 0.20 | -0.07 | -0.19 – 0.04 | -0.06 | -0.18 – 0.07 | -0.05 | -0.16 – 0.08 | -0.00 | -0.05 – 0.05 | -0.00 | -0.04 – 0.03 | -0.53 | -0.85 – -0.22 | -0.15 | -0.41 – 0.10 | -0.06 | -0.18 – 0.05 | -0.06 | -0.18 – 0.06 | -0.01 | -0.13 – 0.10 | -0.00 | -0.04 – 0.04 | -0.00 | -0.03 – 0.03 |
| dummy_gardneraltman_halfeye_y | 0.29 | -0.06 – 0.66 | -0.03 | -0.30 – 0.24 | 0.54 | 0.42 – 0.66 | 0.40 | 0.27 – 0.54 | 0.39 | 0.27 – 0.51 | -0.79 | -0.83 – -0.74 | -0.32 | -0.37 – -0.28 | ||||||||||||||||||||||||||||||||||||||||
| dummy_raincloud_halfeye_x | 0.21 | -0.06 – 0.49 | 0.25 | 0.11 – 0.38 | 0.10 | -0.04 – 0.24 | 0.19 | 0.06 – 0.31 | 0.15 | 0.10 – 0.20 | -0.12 | -0.16 – -0.08 | ||||||||||||||||||||||||||||||||||||||||||
| dummy_raincloud_halfeye_y | 0.35 | 0.01 – 0.70 | -0.05 | -0.35 – 0.24 | 0.43 | 0.30 – 0.56 | 0.25 | 0.11 – 0.40 | 0.24 | 0.11 – 0.37 | 0.00 | -0.05 – 0.05 | -0.23 | -0.27 – -0.19 | ||||||||||||||||||||||||||||||||||||||||
| Random Effects | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| σ2 | 0.64 | 0.95 | 0.43 | 0.46 | 0.40 | 0.35 | 0.58 | 0.78 | 0.83 | 0.47 | 0.55 | 0.47 | 0.25 | 0.58 | 0.98 | 0.55 | 0.62 | 0.50 | 0.31 | 0.54 | 0.78 | 0.93 | 0.54 | 0.60 | 0.53 | 0.26 | 0.51 | |||||||||||||||||||||||||||
| τ00 | 0.27 session | 0.07 session | 0.59 session | 0.57 session | 0.63 session | 0.60 session | 0.44 session | 0.21 session | 0.22 session | 0.51 session | 0.45 session | 0.52 session | 0.72 session | 0.42 session | 0.04 session | 0.48 session | 0.42 session | 0.55 session | 0.78 session | 0.50 session | 0.15 session | 0.10 session | 0.46 session | 0.42 session | 0.50 session | 0.86 session | 0.52 session | |||||||||||||||||||||||||||
| ICC | 0.29 | 0.07 | 0.58 | 0.55 | 0.62 | 0.63 | 0.43 | 0.21 | 0.21 | 0.52 | 0.45 | 0.53 | 0.74 | 0.42 | 0.04 | 0.47 | 0.40 | 0.53 | 0.71 | 0.48 | 0.16 | 0.09 | 0.46 | 0.41 | 0.48 | 0.77 | 0.51 | |||||||||||||||||||||||||||
| N | 18 session | 31 session | 40 session | 40 session | 40 session | 39 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 40 session | 31 session | 40 session | 40 session | 40 session | 39 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 40 session | |||||||||||||||||||||||||||
| Observations | 109 | 185 | 480 | 480 | 480 | 1824 | 5472 | 104 | 177 | 480 | 480 | 480 | 1824 | 5472 | 195 | 480 | 480 | 480 | 1824 | 5472 | 107 | 187 | 480 | 480 | 480 | 1824 | 5472 | |||||||||||||||||||||||||||
| Marginal R2 / Conditional R2 | 0.169 / 0.393 | 0.014 / 0.081 | 0.036 / 0.579 | 0.019 / 0.543 | 0.032 / 0.608 | 0.118 / 0.653 | 0.010 / 0.418 | 0.106 / 0.269 | 0.008 / 0.205 | 0.074 / 0.540 | 0.042 / 0.452 | 0.040 / 0.532 | 0.151 / 0.752 | 0.027 / 0.419 | 0.018 / 0.055 | 0.018 / 0.460 | 0.005 / 0.385 | 0.011 / 0.510 | 0.006 / 0.688 | 0.004 / 0.461 | 0.130 / 0.262 | 0.013 / 0.109 | 0.050 / 0.467 | 0.018 / 0.408 | 0.015 / 0.472 | 0.000 / 0.744 | 0.013 / 0.487 | |||||||||||||||||||||||||||
## Gardner Altman Plot vs. Raincloud Plot
gardneraltman_vs_raincloud <- study1_w %>%
group_by(session) %>%
mutate(dummy_gardneraltman_raincloud =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "raincloud_yaxis") %>%
ungroup()
gardneraltman_vs_raincloud_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_gardneraltman_raincloud =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "raincloud_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_gardneraltman_raincloud <-
brm(scale(rating_u3) ~ dummy_gardneraltman_raincloud + effsize + (1|session),
data = gardneraltman_vs_raincloud%>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_gardneraltman_raincloud <-
brm(scale(rating_ov) ~ dummy_gardneraltman_raincloud + effsize + (1|session),
data = gardneraltman_vs_raincloud%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_diffi_gardneraltman_raincloud <-
brm(scale(diffi) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_infor_gardneraltman_raincloud <-
brm(scale(infor) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_gardneraltman_raincloud <-
brm(scale(value) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic01_gardneraltman_raincloud <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_effic0306_gardneraltman_raincloud <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)## Halfeye xaxis vs. Halfeye yaxis
halfeye_x_vs_halfeye_y <- study1_w %>%
group_by(session) %>%
mutate(dummy_halfeye_x_halfeye_y =
case_when(type == "halfeye_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_xaxis" | type == "halfeye_yaxis") %>%
ungroup()
halfeye_x_vs_halfeye_y_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_halfeye_x_halfeye_y =
case_when(type == "halfeye_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_xaxis" | type == "halfeye_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_halfeye_x_halfeye_y <-
brm(scale(rating_u3) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y %>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_halfeye_x_halfeye_y <-
brm(scale(rating_ov) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 20000)mod_diffi_halfeye_x_halfeye_y <-
brm(scale(diffi) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y,
iter = 10000,
save_pars = save_pars(all = TRUE))
mod_infor_halfeye_x_halfeye_y <-
brm(scale(infor) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_halfeye_x_halfeye_y <-
brm(scale(value) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_effic01_halfeye_x_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
iter = 10000)mod_effic0306_halfeye_x_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y_effic%>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)sjPlot::tab_model(mod_accu3_halfeye_x_halfeye_y, mod_accov_halfeye_x_halfeye_y,
mod_diffi_halfeye_x_halfeye_y, mod_infor_halfeye_x_halfeye_y,
mod_value_halfeye_x_halfeye_y,
mod_effic01_halfeye_x_halfeye_y)| scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | -0.09 | -0.41 – 0.24 | 0.11 | -0.10 – 0.32 | -0.10 | -0.35 – 0.15 | -0.07 | -0.34 – 0.17 | -0.03 | -0.29 – 0.24 | 0.11 | -0.17 – 0.38 |
| dummy_halfeye_x_halfeye_y | 0.16 | -0.16 – 0.50 | -0.23 | -0.51 – 0.06 | 0.20 | 0.08 – 0.33 | 0.16 | 0.04 – 0.28 | 0.05 | -0.06 – 0.16 | -0.14 | -0.20 – -0.08 |
| effsize | -0.67 | -0.97 – -0.38 | -0.04 | -0.31 – 0.22 | -0.01 | -0.12 – 0.10 | 0.04 | -0.07 – 0.15 | 0.05 | -0.05 – 0.16 | -0.00 | -0.05 – 0.05 |
| Random Effects | ||||||||||||
| σ2 | 0.71 | 0.98 | 0.48 | 0.47 | 0.41 | 0.37 | ||||||
| τ00 | 0.25 session | 0.03 session | 0.55 session | 0.58 session | 0.65 session | 0.72 session | ||||||
| ICC | 0.26 | 0.03 | 0.53 | 0.55 | 0.61 | 0.66 | ||||||
| N | 18 session | 31 session | 40 session | 40 session | 40 session | 39 session | ||||||
| Observations | 109 | 194 | 480 | 480 | 480 | 1824 | ||||||
| Marginal R2 / Conditional R2 | 0.148 / 0.334 | 0.019 / 0.052 | 0.012 / 0.521 | 0.008 / 0.538 | 0.002 / 0.598 | 0.005 / 0.626 | ||||||
sjPlot::tab_model(mod_effic0306_halfeye_x_halfeye_y,
mod_accu3_gardneraltman_raincloud,
mod_accov_gardneraltman_raincloud,
mod_diffi_gardneraltman_raincloud)| scale(log_effic_05righttrunc) | scale(rating_u3) | scale(rating_ov) | scale(diffi) | |||||
|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 0.08 | -0.17 – 0.30 | 0.12 | -0.21 – 0.45 | 0.01 | -0.23 – 0.27 | -0.06 | -0.29 – 0.16 |
| dummy_halfeye_x_halfeye_y | -0.12 | -0.16 – -0.09 | ||||||
| effsize | 0.00 | -0.03 – 0.03 | -0.69 | -0.98 – -0.40 | -0.10 | -0.35 – 0.14 | -0.09 | -0.22 – 0.03 |
| dummy_gardneraltman_raincloud | -0.18 | -0.48 – 0.15 | -0.01 | -0.30 – 0.28 | 0.14 | -0.00 – 0.27 | ||
| Random Effects | ||||||||
| σ2 | 0.52 | 0.69 | 0.89 | 0.59 | ||||
| τ00 | 0.52 session | 0.24 session | 0.14 session | 0.45 session | ||||
| ICC | 0.50 | 0.26 | 0.14 | 0.43 | ||||
| N | 40 session | 18 session | 31 session | 40 session | ||||
| Observations | 5472 | 107 | 178 | 480 | ||||
| Marginal R2 / Conditional R2 | 0.004 / 0.482 | 0.159 / 0.353 | 0.010 / 0.148 | 0.009 / 0.420 | ||||
sjPlot::tab_model(mod_infor_gardneraltman_raincloud,
mod_value_gardneraltman_raincloud,
mod_effic01_gardneraltman_raincloud,
mod_effic0306_gardneraltman_raincloud)| scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | |||||
|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | -0.09 | -0.32 – 0.14 | -0.06 | -0.31 – 0.18 | 0.41 | 0.16 – 0.68 | 0.05 | -0.18 – 0.27 |
| dummy_gardneraltman_raincloud | 0.16 | 0.01 – 0.30 | 0.15 | 0.02 – 0.28 | -0.77 | -0.81 – -0.72 | -0.10 | -0.14 – -0.06 |
| effsize | -0.11 | -0.24 – 0.01 | -0.11 | -0.22 – 0.00 | 0.00 | -0.04 – 0.04 | 0.00 | -0.04 – 0.04 |
| Random Effects | ||||||||
| σ2 | 0.60 | 0.50 | 0.25 | 0.57 | ||||
| τ00 | 0.44 session | 0.53 session | 0.70 session | 0.45 session | ||||
| ICC | 0.42 | 0.52 | 0.73 | 0.44 | ||||
| N | 40 session | 40 session | 39 session | 40 session | ||||
| Observations | 480 | 480 | 1824 | 5472 | ||||
| Marginal R2 / Conditional R2 | 0.012 / 0.407 | 0.010 / 0.505 | 0.144 / 0.747 | 0.003 / 0.426 | ||||